Triple
T3624309
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michelle Gomez |
E76799
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Gomez |
E172577
|
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: Gomez | Statement: [Michelle Gomez, familyName, Gomez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gomez Context triple: [Michelle Gomez, familyName, Gomez]
-
A.
Gomez
chosen
Gomez is a common Spanish-origin surname borne by numerous notable individuals across fields such as entertainment, sports, and politics.
-
B.
Herculez Gomez
Herculez Gomez is a retired American soccer forward known for his goal-scoring in Major League Soccer and appearances with the United States national team.
-
C.
Armando
Armando is a masculine given name of Spanish and Portuguese origin, commonly used in many Spanish-speaking countries.
-
D.
Gus
Gus is the lovable, chubby mouse in Disney's 1950 animated film "Cinderella," known for his comic relief and loyal friendship to Cinderella.
-
E.
Gus
Gus is a character from T. S. Eliot's "Old Possum's Book of Practical Cats," depicted as an elderly, once-famous theater cat reflecting nostalgically on his past glory.
- 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_69ad85dc03948190b35b7189e4175bcc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc2d9845c8190ad65b2471000dfa0 |
completed | March 8, 2026, 6:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4332260cc8190964a15bfee0a3b61 |
completed | March 13, 2026, 3:54 p.m. |
Created at: March 8, 2026, 3:23 p.m.