Triple
T11119559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bain & Company |
E262979
|
entity |
| Predicate | hasAlumni |
P51
|
FINISHED |
| Object |
Orit Gadiesh
Orit Gadiesh is an Israeli-American businesswoman and longtime chair of the global management consulting firm Bain & Company, known for her influence in corporate strategy and leadership.
|
E912311
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Orit Gadiesh | Statement: [Bain & Company, hasAlumni, Orit Gadiesh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orit Gadiesh Context triple: [Bain & Company, hasAlumni, Orit Gadiesh]
-
A.
Daphna Kastner
Daphna Kastner is a Canadian actress, screenwriter, and director known for her work in independent films.
-
B.
Gila Almagor
Gila Almagor is a renowned Israeli actress, author, and film producer often referred to as the "first lady of Israeli cinema and theatre."
-
C.
Orna Kupferman
Orna Kupferman is an Israeli computer scientist known for her contributions to formal verification, automata theory, and logic in computer science.
-
D.
Nami Melumad
Nami Melumad is an Israeli-Dutch film and television composer known for her work on major franchises including the Marvel Cinematic Universe and Star Trek.
-
E.
Vered Bar-El
Vered Bar-El is a character featured in the comic book series "The Source."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Orit Gadiesh Triple: [Bain & Company, hasAlumni, Orit Gadiesh]
Generated description
Orit Gadiesh is an Israeli-American businesswoman and longtime chair of the global management consulting firm Bain & Company, known for her influence in corporate strategy and leadership.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Orit Gadiesh Target entity description: Orit Gadiesh is an Israeli-American businesswoman and longtime chair of the global management consulting firm Bain & Company, known for her influence in corporate strategy and leadership.
-
A.
Daphna Kastner
Daphna Kastner is a Canadian actress, screenwriter, and director known for her work in independent films.
-
B.
Gila Almagor
Gila Almagor is a renowned Israeli actress, author, and film producer often referred to as the "first lady of Israeli cinema and theatre."
-
C.
Orna Kupferman
Orna Kupferman is an Israeli computer scientist known for her contributions to formal verification, automata theory, and logic in computer science.
-
D.
Nami Melumad
Nami Melumad is an Israeli-Dutch film and television composer known for her work on major franchises including the Marvel Cinematic Universe and Star Trek.
-
E.
Vered Bar-El
Vered Bar-El is a character featured in the comic book series "The Source."
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d6aa9b46cc8190b19f9f0cc45bf322 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79af7b72c8190a19dbcbb3a69fb5b |
completed | April 9, 2026, 12:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e4ace2228c8190936757f5b1eaa1eb |
completed | April 19, 2026, 10:22 a.m. |
| NEDg | Description generation | batch_69e4b12c04e48190ad7546d556a5109f |
completed | April 19, 2026, 10:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e4b2949c7c8190820b7f1f87e00602 |
completed | April 19, 2026, 10:46 a.m. |
Created at: April 8, 2026, 9:28 p.m.