Triple
T10267424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1913 Woman Suffrage Procession in Washington, D.C. |
E240744
|
entity |
| Predicate | spectatorCount |
P26094
|
FINISHED |
| Object | tens of thousands of spectators |
—
|
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: tens of thousands of spectators | Statement: [1913 Woman Suffrage Procession in Washington, D.C., spectatorCount, tens of thousands of spectators]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spectatorCount Context triple: [1913 Woman Suffrage Procession in Washington, D.C., spectatorCount, tens of thousands of spectators]
-
A.
hasSpectators
Indicates that an event, activity, or occurrence is being watched or attended by one or more spectators.
-
B.
spectatorType
Indicates the role or category of a spectator in relation to an event or activity.
-
C.
spectatorsInBubbleGames
Indicates that the games played within a controlled "bubble" environment had spectators present.
-
D.
spectatorSport
Indicates that an activity is primarily performed for the enjoyment of an audience watching rather than for direct participation.
-
E.
fanAttendance
chosen
Indicates the number or presence of fans attending an event, such as a game, show, or performance.
- 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_69d381a94c1881908fc38fc263d9b9c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2872830819080fdfa816167d04c |
completed | April 7, 2026, 9:46 a.m. |
| PD | Predicate disambiguation | batch_69d4d1ef6e6c81908a8ee52e4d28127b |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:34 a.m.