Triple
T5508721
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tim Duncan |
E144508
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Timothy |
E5498
|
NE FINISHED |
How this triple was built (2 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: Timothy | Statement: [Tim Duncan, givenName, Timothy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Timothy Context triple: [Tim Duncan, givenName, Timothy]
-
A.
Timothy
chosen
Timothy is the given first name of Sir Tim Berners-Lee, the British computer scientist who invented the World Wide Web.
-
B.
Timothy
Timothy is a prominent early Christian companion and protégé of the Apostle Paul, known from the New Testament for his missionary work and pastoral leadership.
-
C.
Timothy
Timothy is a character known primarily as the adversary of Charly Baltimore in the action thriller film "The Long Kiss Goodnight."
-
D.
Tobias
Tobias was a Native American man from the 17th-century Wampanoag community, known primarily through his familial connection to the Sakonnet leader Awashonks.
-
E.
Tobias
Tobias is the full given name of Toby Ziegler, the fictional White House Communications Director from the television series "The West Wing."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c008f6b5048190a09064116062cf69 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f4a80d88190bab0056c4c78be93 |
completed | March 22, 2026, 4:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c027c4b6c0819086c7c64911c7106e |
completed | March 22, 2026, 5:32 p.m. |
Created at: March 22, 2026, 3:33 p.m.