Triple
T13461588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hunting Lane Films |
E311381
|
entity |
| Predicate | roleInBlueValentine |
P109596
|
FINISHED |
| Object | production company |
—
|
LITERAL 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: production company | Statement: [Hunting Lane Films, roleInBlueValentine, production company]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInBlueValentine Context triple: [Hunting Lane Films, roleInBlueValentine, production company]
-
A.
roleInRomeoAndJuliet
Indicates the specific character or part that an entity plays in the work "Romeo and Juliet."
-
B.
roleInDialogue
Indicates that an entity participates in a dialogue with a specific conversational role (e.g., speaker, listener, moderator) relative to other participants.
-
C.
roleInFrancesHa
Indicates that one entity plays a specific role or character in the film "Frances Ha" in relation to another entity.
-
D.
roleInScene
Indicates that an entity participates in a particular scene with a specific role or function within that scene.
-
E.
theaterRole
Indicates that an entity holds or performs a specific role or character in a theatrical production in relation to another entity (such as a play or performance).
- F. None of above. chosen
Provenance (4 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_69d806a938b8819097ec43a2229fc7f9 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf0d95fc81909d9f73d5315dc7b4 |
completed | April 12, 2026, 2:41 p.m. |
| PD | Predicate disambiguation | batch_69d9a03dcd0c8190a8927eb4eaad1c45 |
completed | April 11, 2026, 1:13 a.m. |
| PDg | Predicate description generation | batch_69dadce235f88190a6433395d2969811 |
completed | April 11, 2026, 11:44 p.m. |
Created at: April 9, 2026, 9:41 p.m.