Triple
T37083647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Julie Powell (Julie & Julia character) |
E918223
|
entity |
| Predicate | notableDishPrepared |
P5292
|
FINISHED |
| Object | boeuf bourguignon |
E92863
|
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: boeuf bourguignon | Statement: [Julie Powell (Julie & Julia character), notableDishPrepared, boeuf bourguignon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableDishPrepared Context triple: [Julie Powell (Julie & Julia character), notableDishPrepared, boeuf bourguignon]
-
A.
servesDish
Indicates that one entity prepares and presents a specific dish as food for another entity.
-
B.
preparesFoodFor
Indicates that one entity makes or cooks food intended to be eaten by another entity.
-
C.
culinaryStatus
Indicates the current state or condition of something in relation to cooking or food preparation (e.g., raw, cooked, undercooked, burnt).
-
D.
intendedMeal
Indicates that one entity is the meal that another entity plans or expects to eat.
-
E.
isCookedBy
chosen
Indicates that something has been prepared or made ready for eating through cooking by a particular agent.
- F. None of above.
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_69f76e9952b88190a6fe01ba01476520 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3efdc6b29481909666206a2ad378c1 |
completed | June 26, 2026, 10:31 p.m. |
| PD | Predicate disambiguation | batch_6a037a10036481909c71188b2a0e7f04 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:14 p.m.