Triple
T19103390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Annaprasadam (free meals) department |
E467590
|
entity |
| Predicate | foodPolicy |
P134387
|
FINISHED |
| Object | strictly vegetarian |
—
|
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: strictly vegetarian | Statement: [Annaprasadam (free meals) department, foodPolicy, strictly vegetarian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: foodPolicy Context triple: [Annaprasadam (free meals) department, foodPolicy, strictly vegetarian]
-
A.
policyFocus
Indicates that an entity (such as a person, organization, or document) is primarily concerned with, directed toward, or centered on a particular policy area or issue.
-
B.
commonPolicyArea
Indicates that two entities share the same policy domain, topic, or area of regulatory or legislative focus.
-
C.
nationalPolicy
Indicates that an entity establishes, embodies, or is governed by an official policy at the level of a nation-state.
-
D.
administrationWithFood
Indicates that a substance (such as a medication) is to be taken together with food or during a meal.
-
E.
foodCulture
Indicates the relationship between a place or group and its characteristic traditions, practices, and preferences surrounding food and eating.
- 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_69d8dd05ac4c8190b1967d8f97f3fb2f |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e36f84048190a62c52411eb55411 |
completed | April 20, 2026, 8:27 a.m. |
| PD | Predicate disambiguation | batch_69e4b9ac41848190afd0f33b42cebe99 |
completed | April 19, 2026, 11:17 a.m. |
| PDg | Predicate description generation | batch_69e4bfe8a06081909fd5c28a33e9f218 |
completed | April 19, 2026, 11:43 a.m. |
Created at: April 10, 2026, 12:04 p.m.