Triple
T3224892
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tommy |
E67600
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | Tom |
E128299
|
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: Tom | Statement: [Tommy, relatedName, Tom]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Context triple: [Tommy, relatedName, Tom]
-
A.
Tom
chosen
Tom is a common masculine given name, often used in English-speaking countries as a short form of Thomas.
-
B.
TOM
TOM is the ICAO airline designator used to identify TUI Airways in international aviation operations.
-
C.
Tim
Tim is the given name of Tim Wu, a prominent legal scholar and policy advocate known for coining the term "net neutrality."
-
D.
Tony
Tony is a fictional character from the animated series "Wild Target," known for his adventurous role within the show's ensemble cast.
-
E.
Tony
Tony is the idealistic young protagonist of the musical *West Side Story*, whose forbidden love for Maria drives the story’s modern retelling of *Romeo and Juliet* in 1950s New York.
- 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_69ad858c61888190a31196310d9b30b5 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adae1c51a48190b4a395650528b5d8 |
completed | March 8, 2026, 5:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b27726f76c819092a199ae07a7e688 |
completed | March 12, 2026, 8:19 a.m. |
Created at: March 8, 2026, 3:08 p.m.