Triple
T9753933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lightning McQueen |
E236507
|
entity |
| Predicate | friend |
P8712
|
FINISHED |
| Object | Mater |
E236509
|
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: Mater | Statement: [Lightning McQueen, friend, Mater]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mater Context triple: [Lightning McQueen, friend, Mater]
-
A.
Mater
chosen
Mater is the lovable, rusty tow truck from Pixar's Cars franchise, known for his goofy personality, loyalty to Lightning McQueen, and comic relief.
-
B.
Lola
Lola is a fictional character portrayed by British actor Chiwetel Ejiofor.
-
C.
Lola
"Lola" is a 1970 rock song by The Kinks, famous for its catchy melody and narrative about a romantic encounter that plays with themes of gender identity and ambiguity.
-
D.
Lola
Lola is a 1981 West German drama film directed by Rainer Werner Fassbinder, in which Armin Mueller-Stahl plays a prominent role in a story set in postwar Germany.
-
E.
Lola
Lola is the charismatic drag queen and performer who serves as the central catalyst for change in the musical and film "Kinky Boots."
- 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_69ca84d4eddc8190996fec1417d2bae8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9fb01ad08190b2435fa505c622bc |
completed | April 1, 2026, 10:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1b0288b808190821287a2cc54025d |
completed | April 5, 2026, 12:43 a.m. |
Created at: March 30, 2026, 8:24 p.m.