Triple
T13754853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Telem (Israel) |
E330448
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
Telem
Telem is a small Israeli settlement whose name is derived from the biblical term “Telem.”
|
E1060707
|
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: Telem | Statement: [Telem (Israel), namedAfter, Telem]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Telem Context triple: [Telem (Israel), namedAfter, Telem]
-
A.
Teles
Teles is a relatively obscure figure in Greek mythology, known primarily as one of the many children in the royal lineage associated with the hero Perseus.
-
B.
Telê
Telê is the given name of Telê Santana, a renowned Brazilian football manager best known for coaching Brazil’s celebrated 1982 and 1986 World Cup teams.
-
C.
TELT
TELT is the binational public company responsible for designing, building, and operating the cross-border base tunnel of the Lyon–Turin high-speed rail link between France and Italy.
-
D.
Telegin
Telegin is a minor but memorable character in Anton Chekhov’s play "Uncle Vanya," known for his shabby gentility, loyalty, and melancholy humor.
-
E.
Telecip
Telecip is a French film production company best known for backing the 1976 Academy Award–winning war satire "Black and White in Color."
- 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: Telem Triple: [Telem (Israel), namedAfter, Telem]
Generated description
Telem is a small Israeli settlement whose name is derived from the biblical term “Telem.”
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Telem Target entity description: Telem is a small Israeli settlement whose name is derived from the biblical term “Telem.”
-
A.
Teles
Teles is a relatively obscure figure in Greek mythology, known primarily as one of the many children in the royal lineage associated with the hero Perseus.
-
B.
Telê
Telê is the given name of Telê Santana, a renowned Brazilian football manager best known for coaching Brazil’s celebrated 1982 and 1986 World Cup teams.
-
C.
TELT
TELT is the binational public company responsible for designing, building, and operating the cross-border base tunnel of the Lyon–Turin high-speed rail link between France and Italy.
-
D.
Telegin
Telegin is a minor but memorable character in Anton Chekhov’s play "Uncle Vanya," known for his shabby gentility, loyalty, and melancholy humor.
-
E.
Telecip
Telecip is a French film production company best known for backing the 1976 Academy Award–winning war satire "Black and White in Color."
- 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_69d81c573f288190aa2403d484fa3d49 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02179c948190a652cc8c586e418f |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a859e6748190aa1899830a02b710 |
completed | May 3, 2026, 7:56 p.m. |
| NEDg | Description generation | batch_69f7a91deb3c8190ad2be7f1ca99ac9b |
completed | May 3, 2026, 7:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7ad54e3e88190aeae31d69788cce5 |
completed | May 3, 2026, 8:17 p.m. |
Created at: April 9, 2026, 10:09 p.m.