Triple
T2381319
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pont de l’Alma |
E46316
|
entity |
| Predicate | nearbyEventSite |
P36296
|
FINISHED |
| Object | site of Princess Diana’s fatal 1997 car crash |
—
|
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: site of Princess Diana’s fatal 1997 car crash | Statement: [Pont de l’Alma, nearbyEventSite, site of Princess Diana’s fatal 1997 car crash]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyEventSite Context triple: [Pont de l’Alma, nearbyEventSite, site of Princess Diana’s fatal 1997 car crash]
-
A.
notableNearbySite
Indicates that one entity is a significant or noteworthy site located close to another entity.
-
B.
nearbyVenue
Indicates that one venue is located close to another venue in physical space.
-
C.
nearbyWorldHeritageSite
Indicates that one entity is located close to, or in the immediate vicinity of, a designated World Heritage Site.
-
D.
eventLocationOfNotableActivity
chosen
Indicates that a location is the place where a notable or significant activity or event involving the related entity occurred.
-
E.
nearbyFrontier
Indicates that one entity is located close to a boundary or frontier region associated with another entity.
- F. None of above.
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_69a88a1554a48190a0180682bcf099be |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abc7b7c9188190a824e4b469bc1548 |
completed | March 7, 2026, 6:37 a.m. |
| PD | Predicate disambiguation | batch_69abc59f73f08190924a36d7d475d8f4 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:57 p.m.