Triple
T17108864
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erk Kala |
E415171
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object |
Mary
Mary is a city in southeastern Turkmenistan that serves as a major regional center for the country’s natural gas and cotton industries.
|
E80030
|
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: Mary | Statement: [Erk Kala, nearbyCity, Mary]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mary Context triple: [Erk Kala, nearbyCity, Mary]
-
A.
Mary
Mary is the given name of Mary Jo Kopechne, the American political campaign specialist who died in the 1969 Chappaquiddick incident involving Senator Ted Kennedy.
-
B.
Mary
Mary is the middle name of Edith Tolkien, the wife of author J.R.R. Tolkien.
-
C.
Mary
Mary is the central protagonist of the play "The Memory of Water," around whom the story’s emotional and familial conflicts revolve.
-
D.
Mary
Mary is the birth name of American actress, comedian, and writer Lily Tomlin, known for her groundbreaking work in television, film, and theater.
-
E.
Mary
Mary is the given name of the American stage and film actress Josephine Hull, known for her roles in classic mid-20th-century theater and cinema.
- 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: Mary Triple: [Erk Kala, nearbyCity, Mary]
Generated description
Mary is a city in southeastern Turkmenistan that serves as a major regional center for the country’s natural gas and cotton industries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mary Target entity description: Mary is a city in southeastern Turkmenistan that serves as a major regional center for the country’s natural gas and cotton industries.
-
A.
Mary
chosen
Mary is a significant urban and economic center in southeastern Turkmenistan, known for its role in the country’s natural gas and cotton industries.
-
B.
Mary
Mary is a feminine given name of Hebrew origin, widely used in English-speaking and many other cultures and historically associated with numerous religious and historical figures.
-
C.
Mary
Mary is a character in the "Tunnel community" setting, known as one of the individuals living within its underground society.
-
D.
Mary
Mary is a 1927 psychological novel by Vladimir Nabokov that explores memory, exile, and lost love through the reflections of a Russian émigré in Berlin.
-
E.
Mary
Mary is the given name of Mary Harriman Rumsey, an American social reformer and founder of the Junior League.
- F. None of above.
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_69d886d090cc8190a39cb94992586905 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dc2906a081909d0d43cf04319f52 |
completed | April 18, 2026, 7:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a013a03e2e48190a0b631dd8f6f8a24 |
completed | May 11, 2026, 2:08 a.m. |
| NEDg | Description generation | batch_6a013ae388548190b09d2c81e1ab0d02 |
completed | May 11, 2026, 2:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a013b4df74c81908b3b99e276531e13 |
completed | May 11, 2026, 2:13 a.m. |
Created at: April 10, 2026, 5:35 a.m.